A user's interests and attributes may be inferred from his or her social neighbors. See, for example, A. Mislove et al., “You Are Who You Know: Inferring User Profiles in Online Social Networks,” ACM Web Search and Data Mining Conf., 251-60, (2010). A user's social neighbors refer to the people that he or she interacts with via any communication methods (e.g., face-to-face meeting, email or online social networks). Such inferred interests can enable applications to provide personalized results to meet individual user needs, even when the user is new to an application.
Recently, the proliferation of online social networks has sparked an interest in leveraging social network information to infer the interests of a user, based on the existence of social influence and correlation among neighbors in social networks. For applications that can directly observe a user's behavior (e.g., search engine logs), inferring interests from the user's friends in social networks provides potentially useful enhancements. For many other applications, however, it is difficult to observe sufficient behavior of a large number of users.
In such scenarios, inferring user interests from social neighbors can be the only viable solution. For example, for a new user in a social application, the application may only have information about the user's friends who are already using the application. To motivate the new user to actively participate, the application may want to provide personalized recommendations of relevant content. To this end, the application may infer interests of the user from his or her friends.
It is, however, challenging to obtain consistently high quality results in inferring user interests from social neighbors. See, for example, Z. Wen and C.-Y. Lin, “On the Quality of Inferring Interests From Social Neighbors,” ACM Conf. on Knowledge Discovery and Data Mining, 373-82 (2010). The inference quality can vary significantly due to several factors. First, users may not use social applications intensively to leave enough traces to reveal about their social interactions. For example, only a small percentage of users may actively contribute social content using one or more social software tools (e.g., blogs and social bookmarking). Second, users may only reveal a small subset of possible attributes. Different attributes may have a different impact on inferring interests based on social correlation. For example, the social correlation of a group of college students' ages can be much higher than the correlation of their hometowns. In addition, high quality inference results may only be achieved for a small subset of users.
A need therefore remains for improved methods and apparatus for inferring user interests from both direct and indirect social neighbors. A further need exists for inferring user interests from social neighbors by exploiting the correlation among multiple attributes of a user, in addition to the social correlation of an attribute among a group of users.